Abstract
The information retrieval task for statute law requires a system to retrieve the relevant legal articles given a legal bar exam query. The Transformer-based approaches have demonstrated robustness over traditional machine learning and information retrieval methods for legal documents. However, those approaches are mainly domain adaptation without attempting to tackle the challenges in the characteristics of the legal queries and the legal documents. This paper specifies two challenges related to the characteristics of the two legal materials and proposes methods to tackle them effectively. Specifically, the challenge of different language used (while the articles use abstract language, the queries may use the language to describe a specific scenario) is addressed by a specialized model. Besides, another specialized model can overcome the challenge of long articles and queries. As shown in the experimental results, our proposed system achieved a state-of-the-art F2 score of 76.87%, with an improvement of 3.85% compared to the previous best system. The code will be available at https://github.com/nguyenlab/statute_law_IR.
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This work was supported by JSPS Kakenhi Grant Number 20H04295, 20K20406, and 20K20625.
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Nguyen, C., Le, NK., Nguyen, DH., Nguyen, P., Nguyen, LM. (2022). A Legal Information Retrieval System for Statute Law. In: Szczerbicki, E., Wojtkiewicz, K., Nguyen, S.V., Pietranik, M., Krótkiewicz, M. (eds) Recent Challenges in Intelligent Information and Database Systems. ACIIDS 2022. Communications in Computer and Information Science, vol 1716. Springer, Singapore. https://doi.org/10.1007/978-981-19-8234-7_29
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